PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale

TL;DR

This paper introduces PeerFL, a novel simulator integrating peer-to-peer federated learning with NS3 to enable large-scale, heterogeneous device experiments with dynamic network conditions, enhancing research capabilities.

Contribution

It presents the first NS3-based simulator for peer-to-peer federated learning, supporting heterogeneous devices and dynamic WiFi network simulations at scale.

Findings

Simulates up to 450 heterogeneous devices in federated learning.

Efficient in computational resource utilization at large scale.

Open source framework available for community use.

Abstract

This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a critical gap in existing simulation tools, enhancing the overall utility and user experience. NS3 is leveraged to simulate WiFi dynamics to facilitate federated learning experiments with participants that move around physically during training, leading to dynamic network characteristics. Our experiments showcase the simulator's efficiency in computational resource utilization at scale, with a maximum of 450 heterogeneous devices modelled as participants in federated learning. This positions it as a valuable tool for simulation-based investigations in peer-to-peer federated learning. The framework is open source and available for use and extension to the community.